Balanced energy allocation scheme for a solar-powered sensor system and its effects on network-wide performance

Noh, Dong Kun; Kang, Kyungtae

doi:10.1016/j.jcss.2010.08.008

Cited by 59 publications

(42 citation statements)

References 19 publications

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“…The solution proposed by Noh and Kang [94] is similar to previous approaches. They use the EWMA equation to keep track of the solar energy profile observed in the past.…”

Section: Prediction Modelsmentioning

confidence: 76%

Wireless Sensor Networks with Energy Harvesting

Basagni¹,

Naderi²,

Petrioli³

et al. 2013

Mobile Ad Hoc Networking

140

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This chapter covers the fundamental aspects of energy harvesting-based wireless sensor networks (EHWSNs), ranging from the architecture of an EHWSN node and of its energy subsystem, to protocols for task allocation, MAC, and routing, passing through models for predicting energy availability. With the advancement of energy harvesting techniques, along with the development of small factor harvesters for many different energy sources, EHWSNs are poised to become the technology of choice for the host of applications that require the network to function for years or even decades. Through the definition of new hardware and communication protocols specifically tailored to the fundamentally different models of energy availability, new applications can also be conceived that rely on "perennial" functionalities from networks that are truly self-sustaining and with low environmental impact. 703 704 WIRELESS SENSOR NETWORKS WITH ENERGY HARVESTING hardware, protocol stack design, localization and tracking techniques, and energy management [1].Research on WSNs has been driven (and somewhat limited) by a common focus: energy efficiency. Nodes of a WSN are typically powered by batteries. Once their energy is depleted, the node is "dead." Only in very particular applications can batteries be replaced or recharged. However, even when this is possible, the replacement/ recharging operation is slow and expensive and decreases network performance. Different techniques have therefore been proposed to slow down the depletion of battery energy, which include power control and the use of duty cycle-based operation. The latter technique exploits the low power modes of wireless transceivers, whose components can be switched off for energy saving. When the node is in a low power (or "sleep") mode its consumption is significantly lower than when the transceiver is on [2,3]. However, when asleep the node cannot transmit or receive packets. The duty cycle expresses the ratio between the time when the node is on and the sum of the times when the node is on and asleep. Adopting protocols that operate at very low duty cycles is the leading type of solution for enabling long-lasting WSNs [4]. However, this approach suffers from two main drawbacks: (1) There is an inherent tradeoff between energy efficiency (i.e., low duty cycling) and data latency, and (2) battery operated WSNs fail to provide the needed answer to the requirements of many emerging applications that demand network lifetimes of decades or more. Battery leakage and aging deplete batteries within a few years, even if they are seldom used [5,6]. For these reasons, recent research on long-lasting WSNs is taking a different approach, proposing energy harvesters combined with the use of rechargeable batteries and super-capacitors (for energy storage) as the key enabler to "perpetual" WSN operations.Energy-Harvesting-based WSNs (EHWSNs) are the result of endowing WSN nodes with the capability of extracting energy from the surrounding environment. Energy harvesting can exploit different sou...

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“…The solution proposed by Noh and Kang [94] is similar to previous approaches. They use the EWMA equation to keep track of the solar energy profile observed in the past.…”

Section: Prediction Modelsmentioning

confidence: 76%

Wireless Sensor Networks with Energy Harvesting

Basagni¹,

Naderi²,

Petrioli³

et al. 2013

Mobile Ad Hoc Networking

140

View full text Add to dashboard Cite

show abstract

“…Noh et al showed how to use solar energy to maximize the amount of throughput by adaptively controlling the reliability [15]. In addition, they designed simple solar energy allocation (SSEA) and accurate solar energy allocation (ASEA) algorithms to optimally utilize the periodically harvested solar energy, while minimizing the variability of energy allocation [16]. Chen et al investigated the problem of maximizing the throughput over a finite-horizon time period and proposed a three-step approach to solve the problem [17].…”

Section: Related Workmentioning

confidence: 99%

“…Denote by B i,t the remaining energy of sensor node i at slot t. Let ρ i,t and A i,t denote the total amount of harvested energy and energy allocation for sensor node i at slot t, respectively. It is shown in [16] [20] that the sensor nodes can estimate ρ i,t with high accuracy. Their results will be introduced in this paper to estimate ρ i,t .…”

Section: Network Model and Problem Statementmentioning

confidence: 99%

Distributed Sampling Rate Control for Rechargeable Sensor Nodes with Limited Battery Capacity

Zhang

Chen

et al. 2013

IEEE Trans. Wireless Commun.

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Abstract-Energy harvesting is a promising technology for extending the lifetime of battery-powered sensor networks. Due to time variations of harvested energy, one of the main challenging issues is to maximize the uninterrupted sampling rates of all sensor nodes, which represents the network performance. Most of existing works do not consider the limited capacity of rechargeable battery. In this paper, we are concerned with how to adaptively decide the sampling rate for each rechargeable sensor node with a limited battery capacity to maximize the overall network performance. To solve this problem, we firstly propose an adaptive Energy Allocation sCHeme (EACH) for each sensor node to manage its energy use in an efficient way. Then we develop a Distributed Sampling Rate Control (DSRC) algorithm to obtain the optimal sampling rate. Furthermore, an Improved adaptive Energy Allocation sCHeme (IEACH) is proposed to reduce the impact due to imprecise estimation of harvested energy. Extensive simulations using real experimental data obtained from Baseline Measurement System (BMS) of Solar Radiation Research Laboratory are conducted to demonstrate the efficiency of the proposed algorithms.Index Terms-Rechargeable sensor networks, limited battery capacity, energy allocation, sampling rate control.

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“…Copyright is held by the owner/author(s). works have proposed energy predictions methods for energyharvesting WSNs (EH-WSNs) [1,4,5,3]. In this work, we propose a simple yet effective enhancement of the ProEnergy prediction algorithm [1] to improve the accuracy of solar energy predictions, while, at the same time, reducing its memory and energy overhead.…”

Section: Introductionmentioning

confidence: 99%

Improving energy predictions in EH-WSNs with Pro-Energy-VLT

Cammarano

Petrioli

Spenza

2013

Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems

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The increasing popularity of micro-scale energy-scavenging techniques for wireless sensor networks (WSNs) is opening new opportunities for the development of energy-autonomous systems. To sustain perpetual operations, however, environmentally-powered motes must adapt their workload to the stochastic nature of ambient power sources. Energy prediction algorithms, which forecast the source availability and estimate the expected energy intake in the near future, are precious tools to support the development of proactive power management strategies. In this work, we propose Pro-Energy-VLT, an enhancement of the Pro-Energy prediction algorithm that improves the accuracy of energy predictions, while reducing its memory and energy overhead

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Balanced energy allocation scheme for a solar-powered sensor system and its effects on network-wide performance

Cited by 59 publications

References 19 publications

Wireless Sensor Networks with Energy Harvesting

Wireless Sensor Networks with Energy Harvesting

Distributed Sampling Rate Control for Rechargeable Sensor Nodes with Limited Battery Capacity

Improving energy predictions in EH-WSNs with Pro-Energy-VLT

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